Biological visual systems make use of several depth cues (occlusion, texture, perspective, motion, disparity). We propose methods to combine the results of the modules which compute these cues. We describe an ideal depth observer which combines the separate depth cues to absolute depth, and weighting cues relative their estimated reliability. The normative weights assigned to cues should vary with the scene and viewing conditions (e.g., he amount of texture in the scene). An ancillary cue is used to assess the likely performance of various depth modules. We examine the (complicated) mapping between ancillary cues and weights selected for the rule of combination analytically, by comparison with human psychophysical performance, and through adaptive network simulations. We investigate two types of learning; calibration, and depth fusion learning. Calibration translates the output of various modules to veridical depth estimates. Depth fusion learning develops a mapping from ancillary cue values to optimal cue weights. We will develop: (1) psychophysical measurements of the depth combination rule used by human observers when cues are (approximately) in harmony; (2) a software testbed for the simulation and modeling of ideal and psychophysical depth observers; (3) models of the psychophysical observer based on these data and normative ('ideal observer') models; and (4) models of calibration and depth fusion learning. The proposed research will allow us to further understand the use of multiple depth cues by the human visual system. An understanding of the calibration process is immediately applicable to the recalibration that takes place in biological vision when basic parameters change over time, such as interpupillary distance. The research on fusion learning will shed light on how the human visual system can make the most reliable estimates of depth possible as the visual apparatus changes (through aging and/or disease) so as to alter the relative reliability of the cues.